Synchronous Parallel Processing of Big-Data Analytics Services to Optimize Performance in Federated Clouds

Jung, Gueyoung; Gnanasambandam, Nathan; Mukherjee, Tridib

doi:10.1109/cloud.2012.108

Cited by 45 publications

(28 citation statements)

References 14 publications

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“…In [100], authors described a cloud-bursting based on maximally overlapped loadbalancing algorithm to optimize the performance of Big Data analytics that can be run in loosely coupled and distributed computing environments such as federated clouds. To reduce the quantity and the processing time of Big Data sets encountered by the current typical Cloud Big Data processing techniques, in [101], authors proposed a spatiotemporal compression technique-based approach on cloud to deal with Big Data and big graph data from real-world applications.…”

Section: Computer Sciencementioning

confidence: 99%

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A general perspective of Big Data: applications, tools, challenges and trends

et al. 2015

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Big Data has become a very popular term. It refers to the enormous amount of structured, semi-structured and unstructured data that are exponentially generated by high-performance applications in many domains: biochemistry, genetics, molecular biology, physics, astronomy, business, to mention a few. Since the literature of Big Data has increased significantly in recent years, it becomes necessary to develop an overview of the state-of-the-art in Big Data. This paper aims to provide a comprehensive review of Big Data literature of the last 4 years, to identify the main challenges, areas of application, tools and emergent trends of Big Data. To meet this objective, we have analyzed and classified 457 papers concerning Big Data. This review gives relevant B Giner Alor-Hernández

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Section: Computer Sciencementioning

confidence: 99%

“…Some of the issues faced by Big Data researches related to the use of Cloud computing are data privacy [46,98,99], data management [40,55], and efficient processing and analysis of data [100,101].…”

Section: Computer Sciencementioning

confidence: 99%

A general perspective of Big Data: applications, tools, challenges and trends

et al. 2015

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“…A hybrid approach to perform OLAP using GAE and AppScale [83] was provided, using two methods for data synchronisation, namely bulk data transfer and incremental data transfer. Moreover, Jung et al [84] propose optimisations for scheduling and processing of Big Data analysis on federated Clouds. Chang et al [85] examined different data analytics workloads, where results show significant diversity of resource usage (CPU, I/O and, network).…”

Section: Data Processing and Resource Managementmentioning

confidence: 99%

Big Data computing and clouds: Trends and future directions

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Bianchi

et al. 2015

Journal of Parallel and Distributed Computing

674

317

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This paper discusses approaches and environments for carrying out analytics on Clouds for Big Data applications. It revolves around four important areas of analytics and Big Data, namely (i) data management and supporting architectures; (ii) model development and scoring; (iii) visualisation and user interaction; and (iv) business models. Through a detailed survey, we identify possible gaps in technology and provide recommendations for the research community on future directions on Cloud-supported Big Data computing and analytics solutions.

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“…[1] Big data phenomenon refers to the practice of collection and processing of very large data sets and associated systems and algorithms used to analyze these massive datasets. Architectures for big data usually range across multiple machines and clusters, and they commonly consist of multiple special purpose sub-systems.…”

Section: Introductionmentioning

confidence: 99%

Big Data Storage Method in Wireless Communication Environment

Liang¹

2012

Proceedings of the 2012 2nd International Conference on Computer and Information Applications (ICCIA 2012)

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Abstract-Big data phenomenon refers to the practice of collection and processing of very large data sets and associated systems and algorithms used to analyze these massive data sets. Big data service is very attractive in the field of wireless communication environment, especially when we face the spatial applications, which are typical applications of big data. Because of the complexity to ingest, store and analyze geographical information data, this paper reflects on a few of the technical problems presented by the exploration of big data, and puts forward an effective storage method in wireless communication environment, which is based on the measurement of moving regularity through proposing three key techniques: partition technique, index technique and prefetch technique. Experimental results show that the performance of big data storage method using these new techniques is better than the other storage methods on managing a great capacity of big data in wireless communication environment.

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Synchronous Parallel Processing of Big-Data Analytics Services to Optimize Performance in Federated Clouds

Cited by 45 publications

References 14 publications

A general perspective of Big Data: applications, tools, challenges and trends

A general perspective of Big Data: applications, tools, challenges and trends

Big Data computing and clouds: Trends and future directions

Big Data Storage Method in Wireless Communication Environment

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